Homo Deus: a brief History of Tomorrow

particular claim for human pre-eminence.
Deep Blue was given a head start by its creators, who preprogrammed it not
only with the basic rules of chess, but also with detailed instructions regarding
chess strategies. A new generation of AI uses machine learning to do even more
remarkable  and  elegant  things.  In  February  2015  a  program  developed  by
Google  DeepMind  learned  by itself  how  to  play  forty-nine  classic  Atari  games.
One of the developers, Dr Demis Hassabis, explained that ‘the only information
we gave the system was the raw pixels on the screen and the idea that it had to
get a high score. And everything else it had to figure out by itself.’ The program
managed to learn the rules of all the games it was presented with, from Pac-Man
and Space Invaders to car racing and tennis games. It then played most of them
as  well  as  or  better  than  humans,  sometimes  coming  up  with  strategies  that
never occur to human players.
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Deep Blue defeating Garry Kasparov.
© STAN HONDA/AFP/Getty Images.
Computer algorithms have recently proven their worth in ball games, too. For
many decades, baseball teams used the wisdom, experience and gut instincts
of professional scouts and managers to pick players. The best players fetched
millions  of  dollars,  and  naturally  enough  the  rich  teams  got  the  cream  of  the
market, whereas poorer teams had to settle for the scraps. In 2002 Billy Beane,
the manager of the low-budget Oakland Athletics, decided to beat the system.
He  relied  on  an  arcane  computer  algorithm  developed  by  economists  and
computer  geeks  to  create  a  winning  team  from  players  that  human  scouts
overlooked or undervalued. The old-timers were incensed by Beane’s algorithm
transgressing into the hallowed halls of baseball. They said that picking baseball
players  is  an  art,  and  that  only  humans  with  an  intimate  and  long-standing
experience  of  the  game  can  master  it.  A  computer  program  could  never  do  it,
because it could never decipher the secrets and the spirit of baseball.
They  soon  had  to  eat  their  baseball  caps.  Beane’s  shoestring-budget
algorithmic team ($44 million) not only held its own against baseball giants such
as  the  New  York  Yankees  ($125  million),  but  became  the  first  team  ever  in
American  League  baseball  to  win  twenty  consecutive  games.  Not  that  Beane
and  Oakland  could  enjoy  their  success  for  long.  Soon  enough,  many  other
baseball teams adopted the same algorithmic approach, and since the Yankees
and  Red  Sox  could  pay  far  more  for  both  baseball  players  and  computer
software,  low-budget  teams  such  as  the  Oakland  Athletics  now  had  an  even
smaller chance of beating the system than before.
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In 2004 Professor Frank Levy from MIT and Professor Richard Murnane from
Harvard  published  a  thorough  research  of  the  job  market,  listing  those
professions most likely to undergo automation. Truck drivers were given as an

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example of a job that could not possibly be automated in the foreseeable future.
It is hard to imagine, they wrote, that algorithms could safely drive trucks on a
busy road. A mere ten years later, Google and Tesla not only imagine this, but
are actually making it happen.
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In fact, as time goes by, it becomes easier and easier to replace humans with
computer algorithms, not merely because the algorithms are getting smarter, but
also because humans are professionalising. Ancient hunter-gatherers mastered
a  very  wide  variety  of  skills  in  order  to  survive,  which  is  why  it  would  be
immensely difficult to design a robotic hunter-gatherer. Such a robot would have
to  know  how  to  prepare  spear  points  from  flint  stones,  how  to  find  edible
mushrooms in a forest, how to use medicinal herbs to bandage a wound, how to
track  down  a  mammoth  and  how  to  coordinate  a  charge  with  a  dozen  other
hunters.  However,  over  the  last  few  thousand  years  we  humans  have  been
specialising. A taxi driver or a cardiologist specialises in a much narrower niche
than a hunter-gatherer, which makes it easier to replace them with AI.
Even the managers in charge of all these activities can be replaced. Thanks
to its powerful algorithms, Uber can manage millions of taxi drivers with only a
handful of humans. Most of the commands are given by the algorithms without
any need of human supervision.
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In May 2014 Deep Knowledge Ventures – a
Hong  Kong  venture-capital  firm  specialising  in  regenerative  medicine  –  broke
new ground by appointing an algorithm called VITAL to its board. VITAL makes
investment  recommendations  by  analysing  huge  amounts  of  data  on  the
financial  situation,  clinical  trials  and  intellectual  property  of  prospective
companies.  Like  the  other  five  board  members,  the  algorithm  gets  to  vote  on
whether the firm makes an investment in a specific company or not.
Examining VITAL’s record so far, it seems that it has already picked up one
managerial  vice:  nepotism.  It  has  recommended  investing  in  companies  that
grant  algorithms  more  authority.  With  VITAL’s  blessing,  Deep  Knowledge
Ventures  has  recently  invested  in  Silico  Medicine,  which  develops  computer-
assisted  methods  for  drug  research,  and  in  Pathway  Pharmaceuticals,  which
employs  a  platform  called  OncoFinder  to  select  and  rate  personalised  cancer
therapies.
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As  algorithms  push  humans  out  of  the  job  market,  wealth  might  become
concentrated in the hands of the tiny elite that owns the all-powerful algorithms,
creating unprecedented social inequality. Alternatively, the algorithms might not
only manage businesses, but actually come to own them. At present, human law
already recognises intersubjective entities like corporations and nations as ‘legal
persons’. Though Toyota or Argentina has neither a body nor a mind, they are
subject  to  international  laws,  they  can  own  land  and  money,  and  they  can  sue

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and  be  sued  in  court.  We  might  soon  grant  similar  status  to  algorithms.  An
algorithm  could  then  own  a  venture-capital  fund  without  having  to  obey  the
wishes of any human master.
If  the  algorithm  makes  the  right  decisions,  it  could  accumulate  a  fortune,
which  it  could  then  invest  as  it  sees  fit,  perhaps  buying  your  house  and
becoming your landlord. If you infringe on the algorithm’s legal rights – say, by
not paying rent – the algorithm could hire lawyers and sue you in court. If such
algorithms consistently outperform human fund managers, we might end up with
an  algorithmic  upper  class  owning  most  of  our  planet.  This  may  sound
impossible, but before dismissing the idea, remember that most of our planet is
already  legally  owned  by  non-human  inter-subjective  entities,  namely  nations
and  corporations.  Indeed,  5,000  years  ago  much  of  Sumer  was  owned  by
imaginary gods such as Enki and Inanna. If gods can possess land and employ
people, why not algorithms?
So what will people do? Art is often said to provide us with our ultimate (and
uniquely  human)  sanctuary.  In  a  world  where  computers  replace  doctors,
drivers, teachers and even landlords, everyone would become an artist. Yet it is
hard to see why artistic creation will be safe from the algorithms. Why are we so
sure  computers  will  be  unable  to  better  us  in  the  composition  of  music?
According to the life sciences, art is not the product of some enchanted spirit or
metaphysical  soul,  but  rather  of  organic  algorithms  recognising  mathematical
patterns. If so, there is no reason why non-organic algorithms couldn’t master it.
David Cope is a musicology professor at the University of California in Santa
Cruz.  He  is  also  one  of  the  more  controversial  figures  in  the  world  of  classical
music.  Cope  has  written  programs  that  compose  concertos,  chorales,
symphonies  and  operas.  His  first  creation  was  named  EMI  (Experiments  in
Musical  Intelligence),  which  specialised  in  imitating  the  style  of  Johann
Sebastian Bach. It took seven years to create the program, but once the work
was  done,  EMI  composed  5,000  chorales  à  la  Bach  in  a  single  day.  Cope
arranged  a  performance  of  a  few  select  chorales  in  a  music  festival  at  Santa
Cruz.  Enthusiastic  members  of  the  audience  praised  the  wonderful
performance,  and  explained  excitedly  how  the  music  touched  their  innermost
being. They didn’t know it was composed by EMI rather than Bach, and when
the truth was revealed, some reacted with glum silence, while others shouted in
anger.
EMI  continued  to  improve,  and  learned  to  imitate  Beethoven,  Chopin,
Rachmaninov  and  Stravinsky.  Cope  got  EMI  a  contract,  and  its  first  album  –